{"title":"ColoViT：高效网络和视觉变压器的协同整合，用于晚期结肠癌检测。","authors":"Bukka Sathyanarayana, Sreedevi Alampally, Ramakrishna Akella, Veera Venkata Raghunath Indugu","doi":"10.1007/s00432-025-06199-6","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Colon cancer remains a leading cause of cancer-related mortality globally, highlighting the urgent need for advanced diagnostic methods to improve early detection and patient outcomes.</p><p><strong>Methods: </strong>This study introduces ColoViT, a hybrid diagnostic framework that synergistically integrates EfficientNet and Vision Transformers. EfficientNet contributes scalability and high performance in feature extraction, while Vision Transformers effectively capture the global contextual information within colonoscopic images.</p><p><strong>Results: </strong>The integration of these models enables ColoViT to deliver precise and comprehensive image analysis, significantly improving the detection of precancerous lesions and early-stage colon cancers. The proposed model achieved a recall of 92.4%, precision of 98.9%, F1-score of 98.4%, and an AUC of 99% in our preliminary evaluation.</p><p><strong>Conclusion: </strong>ColoViT demonstrates superior performance over existing models, offering a robust solution for enhancing the early detection of colon cancer through deep learning-based image analysis.</p>","PeriodicalId":15118,"journal":{"name":"Journal of Cancer Research and Clinical Oncology","volume":"151 7","pages":"209"},"PeriodicalIF":2.7000,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12241262/pdf/","citationCount":"0","resultStr":"{\"title\":\"ColoViT: a synergistic integration of EfficientNet and vision transformers for advanced colon cancer detection.\",\"authors\":\"Bukka Sathyanarayana, Sreedevi Alampally, Ramakrishna Akella, Veera Venkata Raghunath Indugu\",\"doi\":\"10.1007/s00432-025-06199-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Colon cancer remains a leading cause of cancer-related mortality globally, highlighting the urgent need for advanced diagnostic methods to improve early detection and patient outcomes.</p><p><strong>Methods: </strong>This study introduces ColoViT, a hybrid diagnostic framework that synergistically integrates EfficientNet and Vision Transformers. EfficientNet contributes scalability and high performance in feature extraction, while Vision Transformers effectively capture the global contextual information within colonoscopic images.</p><p><strong>Results: </strong>The integration of these models enables ColoViT to deliver precise and comprehensive image analysis, significantly improving the detection of precancerous lesions and early-stage colon cancers. The proposed model achieved a recall of 92.4%, precision of 98.9%, F1-score of 98.4%, and an AUC of 99% in our preliminary evaluation.</p><p><strong>Conclusion: </strong>ColoViT demonstrates superior performance over existing models, offering a robust solution for enhancing the early detection of colon cancer through deep learning-based image analysis.</p>\",\"PeriodicalId\":15118,\"journal\":{\"name\":\"Journal of Cancer Research and Clinical Oncology\",\"volume\":\"151 7\",\"pages\":\"209\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2025-07-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12241262/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Cancer Research and Clinical Oncology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s00432-025-06199-6\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Cancer Research and Clinical Oncology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s00432-025-06199-6","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ONCOLOGY","Score":null,"Total":0}
ColoViT: a synergistic integration of EfficientNet and vision transformers for advanced colon cancer detection.
Background: Colon cancer remains a leading cause of cancer-related mortality globally, highlighting the urgent need for advanced diagnostic methods to improve early detection and patient outcomes.
Methods: This study introduces ColoViT, a hybrid diagnostic framework that synergistically integrates EfficientNet and Vision Transformers. EfficientNet contributes scalability and high performance in feature extraction, while Vision Transformers effectively capture the global contextual information within colonoscopic images.
Results: The integration of these models enables ColoViT to deliver precise and comprehensive image analysis, significantly improving the detection of precancerous lesions and early-stage colon cancers. The proposed model achieved a recall of 92.4%, precision of 98.9%, F1-score of 98.4%, and an AUC of 99% in our preliminary evaluation.
Conclusion: ColoViT demonstrates superior performance over existing models, offering a robust solution for enhancing the early detection of colon cancer through deep learning-based image analysis.
期刊介绍：
The "Journal of Cancer Research and Clinical Oncology" publishes significant and up-to-date articles within the fields of experimental and clinical oncology. The journal, which is chiefly devoted to Original papers, also includes Reviews as well as Editorials and Guest editorials on current, controversial topics. The section Letters to the editors provides a forum for a rapid exchange of comments and information concerning previously published papers and topics of current interest. Meeting reports provide current information on the latest results presented at important congresses.
The following fields are covered: carcinogenesis - etiology, mechanisms; molecular biology; recent developments in tumor therapy; general diagnosis; laboratory diagnosis; diagnostic and experimental pathology; oncologic surgery; and epidemiology.
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